• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2708-2730
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• 2012

The growing concern for the protection of personal information has made it critical to implement effective technologies for privacy and data management. By observing the limitations of existing approaches, we found that there is an urgent need for a flexible, privacy-aware system that is able to meet the privacy preservation needs at both the role levels and the personal levels. We proposed a conceptual system that considered these two requirements: a graph-based, access control model to safeguard patient privacy. We present a case study of the healthcare field in this paper. While our model was tested in the field of healthcare, it is generic and can be adapted to use in other fields. The proof-of-concept demos were also provided with the aim of valuating the efficacy of our system. In the end, based on the hospital scenarios, we present the experimental results to demonstrate the performance of our system, and we also compared those results to existing privacy-aware systems. As a result, we ensured a high quality of medical care service by preserving patient privacy.

• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.87-94
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• 2013

In this paper, we analyse the weaknesses of authentication scheme preserving user anonymity proposed by Khan et al in 2011 and we propose a new authentication schemes preserving user anonymity that improved these weaknesses. Khan et al's authentication scheme is vulnerable to insider attack and doesn't provide user anonymity to the server. Also, this scheme is still a weakness of wrong password input by mistake in spite of proposing the password change phase. In this paper, we will show that Khan et al's scheme is vulnerable to the stolen smart card attack and the strong server/user masquerade attack. The proposed authentication scheme propose the improved user anonymity, which can provide more secure privacy to user by improving these weaknesses.

• Annual Conference of KIPS
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• 2024.05a
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• pp.229-230
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• 2024

최근 드론으로 극한 환경에서 범죄 수배자 및 실종자를 탐색하는 시도가 활발하다. 이때 생체 인증 기술인 얼굴 인증 기술을 사용하면 탐색 효율이 높아지지만, 암호화되지 않은 인증 프로토콜 적용 시 생체 정보 유출의 위험이 있다. 본 논문에서는 드론이 수집한 얼굴 이미지 템플릿을 암호화하여 안전하게 인증할 수 있는 효율적인 생체 인증 프레임워크인 DF-PPHDM(Privacy-Preserving Hamming Distance biometric Matching for Drone-collected Facial images)을 제안한다. 수집된 얼굴 이미지는 암호문 형태로 서버에 전달되며 서버는 기존 등록된 암호화된 템플릿과의 Hamming distance 분석을 통해 검증한다. 제안한 DF-PPHDM을 RaspberryPI 4B 환경에서 직접 실험하여 분석한 결과, 한정된 리소스를 소유한 드론에서 효율적인 구현이 가능하며, 인증 단계에서 7.83~155.03 ㎲ (microseconds)가 소요된다는 것을 입증하였다. 더불어 서버는 드론이 전송한 암호문으로부터 생체 정보를 복구할 수 없으므로 프라이버시 침해 문제를 예방할 수 있다. 향후 DF-PPHDM에 AI(Artificial Intelligence)를 결합하여 자동화 기능을 추가하고 코드 최적화를 통해 성능을 향상시킬 예정이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.147-169
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• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.197-209
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• 2011

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4226-4241
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• 2014

Cloud storage provides an easy, cost-effective and reliable way of data management for users without the burden of local data storage and maintenance. Whereas, this new paradigm poses many challenges on integrity and privacy of users' data, since users losing grip on their data after outsourcing the data to the cloud server. In order to address these problems, recently, Worku et al. have proposed an efficient privacy-preserving public auditing scheme for cloud storage. However, in this paper, we point out the security flaw existing in the scheme. An adversary, who is on-line and active, is capable of modifying the outsourced data arbitrarily and avoiding the detection by exploiting the security flaw. To fix this security flaw, we further propose a secure and efficient privacy-preserving public auditing scheme, which makes up the security flaw of Worku et al.'s scheme while retaining all the features. Finally, we give a formal security proof and the performance analysis, they show the proposed scheme has much more advantages over the Worku et al.'s scheme.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.102-112
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• 2011

In this paper, we propose a privacy-preserving emergency call scheme, called PEC, enabling patients in life-threatening emergencies to fast and accurately transmit emergency data to the nearby helpers via mobile healthcare social networks (MHSNs). Once an emergency happens, the personal digital assistant (PDA) of the patient runs the PEC to collect the emergency data including emergency location, patient health record, as well as patient physiological condition. The PEC then generates an emergency call with the emergency data inside and epidemically disseminates it to every user in the patient's neighborhood. If a physician happens to be nearby, the PEC ensures the time used to notify the physician of the emergency is the shortest. We show via theoretical analysis that the PEC is able to provide fine-grained access control on the emergency data, where the access policy is set by patients themselves. Moreover, the PEC can withstandmultiple types of attacks, such as identity theft attack, forgery attack, and collusion attack. We also devise an effective revocation mechanism to make the revocable PEC (rPEC) resistant to inside attacks. In addition, we demonstrate via simulation that the PEC can significantly reduce the response time of emergency care in MHSNs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.49-59
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• 2018

Smart grid system has been deployed fast despite of legal, business and technology problems in many countries. One important problem in deploying the smart grid system is to protect private smart meter readings from the unbelievable parties while the major smart meter functions are untouched. Privacy-preserving involves some challenges such as hardware limitations, secure cryptographic schemes and secure signal processing. In this paper, we focused particularly on the smart meter reading aggregation,which is the major research field in the smart meter privacy-preserving. We suggest a noisy weighted aggregation scheme to guarantee differential privacy. The noisy weighted values are generated in such a way that their product is one and are used for making the veiled measurements. In case that a Diffie-Hellman generator is applied to obtain the noisy weighted values, the noisy values are transformed in such a way that their sum is zero. The advantage of Diffie and Hellman group is usually to use 512 bits. Thus, compared to Paillier cryptosystem series which relies on very large key sizes, a significant performance can be obtained.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.741-753
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• 2006

As the total amount of traffic data in network has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical privacy preserving sequential pattern mining method on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model and the retention replacement technique. In addition, our method accelerates the overall mining process by maintaining the meta tables so as to quickly determine whether candidate patterns have ever occurred. The various experiments with real network traffic data revealed tile efficiency of the proposed method.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.209-219
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• 2010

Online social network services that are rapidly growing recently store tremendous data and analyze them for many research areas. To enhance the effectiveness of information, companies or public institutions publish their data and utilize the published data for many purposes. However, a social network containing information of individuals may cause a privacy disclosure problem. Eliminating identifiers such as names is not effective for the privacy protection, since private information can be inferred through the structural information of a social network. In this paper, we consider a new complex attack type that uses both the content and structure information, and propose a model, $\ell$-degree diversity, for the privacy preserving publication of the social network data against such attacks. $\ell$-degree diversity is the first model for applying $\ell$-diversity to social network data publication and through the experiments it shows high data preservation rate.